69 research outputs found

    Rate scalable image compression in the wavelet domain

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    This thesis explores image compression in the wavelet transform domain. This the- sis considers progressive compression based on bit plane coding. The rst part of the thesis investigates the scalar quantisation technique for multidimensional images such as colour and multispectral image. Embedded coders such as SPIHT and SPECK are known to be very simple and e cient algorithms for compression in the wavelet do- main. However, these algorithms require the use of lists to keep track of partitioning processes, and such lists involve high memory requirement during the encoding process. A listless approach has been proposed for multispectral image compression in order to reduce the working memory required. The earlier listless coders are extended into three dimensional coder so that redundancy in the spectral domain can be exploited. Listless implementation requires a xed memory of 4 bits per pixel to represent the state of each transformed coe cient. The state is updated during coding based on test of sig- ni cance. Spectral redundancies are exploited to improve the performance of the coder by modifying its scanning rules and the initial marker/state. For colour images, this is done by conducting a joint the signi cant test for the chrominance planes. In this way, the similarities between the chrominance planes can be exploited during the cod- ing process. Fixed memory listless methods that exploit spectral redundancies enable e cient coding while maintaining rate scalability and progressive transmission. The second part of the thesis addresses image compression using directional filters in the wavelet domain. A directional lter is expected to improve the retention of edge and curve information during compression. Current implementations of hybrid wavelet and directional (HWD) lters improve the contour representation of compressed images, but su er from the pseudo-Gibbs phenomenon in the smooth regions of the images. A di erent approach to directional lters in the wavelet transforms is proposed to remove such artifacts while maintaining the ability to preserve contours and texture. Imple- mentation with grayscale images shows improvements in terms of distortion rates and the structural similarity, especially in images with contours. The proposed transform manages to preserve the directional capability without pseudo-Gibbs artifacts and at the same time reduces the complexity of wavelet transform with directional lter. Fur-ther investigation to colour images shows the transform able to preserve texture and curve.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Development of Novel Image Compression Algorithms for Portable Multimedia Applications

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    Portable multimedia devices such as digital camera, mobile d evices, personal digtal assistants (PDAs), etc. have limited memory, battery life and processing power. Real time processing and transmission using these devices requires image compression algorithms that can compress efficiently with reduced complexity. Due to limited resources, it is not always possible to implement the best algorithms inside these devices. In uncompressed form, both raw and image data occupy an unreasonably large space. However, both raw and image data have a significant amount of statistical and visual redundancy. Consequently, the used storage space can be efficiently reduced by compression. In this thesis, some novel low complexity and embedded image compression algorithms are developed especially suitable for low bit rate image compression using these devices. Despite the rapid progress in the Internet and multimedia technology, demand for data storage and data transmission bandwidth continues to outstrip the capabil- ities of available technology. The browsing of images over In ternet from the image data sets using these devices requires fast encoding and decodin g speed with better rate-distortion performance. With progressive picture build up of the wavelet based coded images, the recent multimedia applications demand goo d quality images at the earlier stages of transmission. This is particularly important if the image is browsed over wireless lines where limited channel capacity, storage and computation are the deciding parameters. Unfortunately, the performance of JPEG codec degrades at low bit rates because of underlying block based DCT transforms. Altho ugh wavelet based codecs provide substantial improvements in progressive picture quality at lower bit rates, these coders do not fully exploit the coding performance at lower bit rates. It is evident from the statistics of transformed images that the number of significant coefficients having magnitude higher than earlier thresholds are very few. These wavelet based codecs code zero to each insignificant subband as it moves from coarsest to finest subbands. It is also demonstrated that there could be six to sev en bit plane passes where wavelet coders encode many zeros as many subbands are likely to be insignificant with respect to early thresholds. Bits indicating insignificance of a coefficient or subband are required, but they don’t code information that reduces distortion of the reconstructed image. This leads to reduction of zero distortion for an increase in non zero bit-rate. Another problem associated with wavelet based coders such as Set partitioning in hierarchical trees (SPIHT), Set partitioning embedded block (SPECK), Wavelet block-tree coding (WBTC) is because of the use of auxiliary lists. The size of list data structures increase exponentially as more and more eleme nts are added, removed or moved in each bitplane pass. This increases the dynamic memory requirement of the codec, which is a less efficient feature for hardware implementations. Later, many listless variants of SPIHT and SPECK, e.g. No list SPIHT (NLS) and Listless SPECK (LSK) respectively are developed. However, these algorithms have similar rate distortion performances, like the list based coders. An improved LSK (ILSK)algorithm proposed in this dissertation that improves the low b it rate performance of LSK by encoding much lesser number of symbols (i.e. zeros) to several insignificant subbands. Further, the ILSK is combined with a block based transform known as discrete Tchebichef transform (DTT). The proposed new coder isnamed as Hierar-chical listless DTT (HLDTT). DTT is chosen over DCT because of it’s similar energy compaction property like discrete cosine transform (DCT). It is demonstrated that the decoded image quality using HLDTT has better visual performance (i.e., Mean Structural Similarity) than the images decoded using DCT based embedded coders in most of the bit rates. The ILSK algorithm is also combined with Lift based wavelet tra nsform to show the superiority over JPEG2000 at lower rates in terms of peak signal-to-noise ratio (PSNR). A full-scalable and random access decodable listless algorithm is also developed which is based on lift based ILSK. The proposed algorithm named as scalable listless embedded block partitioning (S-LEBP) generates bit stream that offer increasing signal-to-noise ratio and spatial resolution. These are very useful features for transmission of images in a heterogeneous network that optimally service each user according to available bandwidth and computing needs. Random access decoding is a very useful feature for extracting/manipulating certain ar ea of an image with minimal decoding work. The idea used in ILSK is also extended to encode and decode color images. The proposed algorithm for coding color images is named as Color listless embedded block partitioning (CLEBP) algorithm. The coding efficiency of CLEBP is compared with Color SPIHT (CSPIHT) and color variant of WBTC algorithm. From the simulation results, it is shown that CLEBP exhibits a significant PSNR performance improvement over the later two algorithms on various types of images. Although many modifications to NLS and LSK have been made, the listless modification to WBTC algorithm has not been reported in the literature. Therefore,a listless variant of WBTC (named as LBTC) algorithm is proposed. LBTC not only reduces the memory requirement by 88-89% but also increases the encoding and decoding speed, while preserving the rate-distortion perform ance at the same time. Further, the combination of DCT with LBTC (named as DCT LBT) and DTT with LBTC (named as Hierarchical listless DTT, HLBTDTT) are compared with some state-of-the-art DCT based embedded coders. It is also shown that the proposed DCT-LBT and HLBT-DTT show significant PSNR improvements over almost all the embedded coders in most of the bit rates. In some multimedia applications e.g., digital camera, camco rders etc., the images always need to have a fixed pre-determined high quality. The extra effort required for quality scalability is wasted. Therefore, non-embedded algo rithms are best suited for these applications. The proposed algorithms can be made non-embedded by encoding a fixed set of bit planes at a time. Instead, a sparse orthogonal transform matrix is proposed, which can be integrated in a JEPG baseline coder. The proposed matrix promises a substantial reduction in hardware complexity with amarginal loss of image quality on a considerable range of bit rates than block based DCT or Integer DCT

    An Efficient MDC based Set Partitioned Embedded Block Image Coding

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    In this paper, fast, efficient, simple and widely used Set Partitioned Embedded bloCK based coding is done onMultiple Descriptions of transformed image. The maximum potential of this type of coding can be exploited with discretewavelet transform (DWT) of images. Two correlated descriptions are generated from a wavelet transformed imageto ensure meaningful transmission of the image over noise prone wireless channels. These correlated descriptions areencoded by set partitioning technique through SPECK coders and transmitted over wireless channels. Quality ofreconstructed image at the decoder side depends upon the number of descriptions received. More the number ofdescriptions received at output side, more enhance the quality of reconstructed image. However, if any of the multipledescription is lost, the receive can estimate it exploiting the correlation between the descriptions. The simulationsperformed on an image on MATLAB gives decent performance and results even after half of the descriptions islost in transmissio

    Contemporary Affirmation of SPIHT Improvements in Image Coding

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    Set partitioning in hierarchal trees (SPIHT) is actually a widely-used compression algorithm for wavelet altered images. On most algorithms developed, SPIHT algorithm from the time its introduction in 1996 for image compression has got lots of interest. Though SPIHT is considerably simpler and efficient than several present compression methods since it's a completely inserted codec, provides good image quality, large PSNR, optimized for modern image transmission, efficient conjunction with error defense, form information on demand and hence element powerful error correction decreases from starting to finish but still it has some downsides that need to be taken away for its better use therefore since its development it has experienced many adjustments in its original model. This document presents a survey on several different improvements in SPIHT in certain fields as velocity, redundancy, quality, error resilience, sophistication, and compression ratio and memory requirement

    The Comprehensive Review of Neural Network: An Intelligent Medical Image Compression for Data Sharing

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    In the healthcare environment, digital images are the most commonly shared information. It has become a vital resource in health care services that facilitates decision-making and treatment procedures. The medical image requires large volumes of storage and the storage scale continues to grow because of the advancement of medical image technology. To enhance the interaction and coordination between healthcare institutions, the efficient exchange of medical information is necessary. Therefore, the sharing of the medical image with zero loss of information and efficiency needs to be guaranteed exactly. Image compression helps ensure that the purpose of sharing this data from a medical image must be as intelligent as possible to contain valuable information while at the same time minimizing unnecessary diagnostic information. Artificial Neural Network has been used to solve many issues in the processing of images. It has proved its dominance in the handling of noisy or incomplete image compression applications over traditional methods. It contributes to the resulting image by a high compression ratio and noise reduction. This paper reviews previous studies on the compression of intelligent medical images with the neural network approach to data sharing

    Real-time scalable video coding for surveillance applications on embedded architectures

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    Data Compression Techniques in Wireless Sensor Networks

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